Cruxly - understand intent

Find actionables in conversations

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Why Cruxly?

To better understand developing scenarios even as the growing volume of content creates challenges in sifting, filtering and identifying actionable information about the future. Examples of intent Example of an event: e.g., an action about a named entity (subject) is described and may involve another named entity (object). E.g., Joe has arrived in Denver.

Who would be interested in knowing about these intents?

Existing and possible approaches

Limitations of current approaches

There is a reason why there are very few intent detection solutions around. It is not an easy problem to solve. There have been attempts made in the email area to prioritize important emails but most of them have been based on finding most frequently used content and conversation patterns to find important emails. There have been some attempt at using NLP solutions they are not common or well-known. Of the intent detection approaches, primary limitations are: Most importantly, the above approaches do not comprehend grammar and language which we believe is what is required for intent detection. Full-scale NLP is probably the most complete method for detecting intent. However, there are many challenges. They are many ambiguities in name entity extraction, understanding the sentence after POS tagging, etc. and ensuring that the analysis can be completed in real time.

Our approach

Cruxly applies a combination of NLP techniques that results in a general purpose commonly sought intent detection in real-time. Key features are: